Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

2013 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao
Keyword(s):  
Decision Making ◽  
Interior Design ◽  
Computational Models ◽  
Human Perception ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Affective States ◽  
Modeling And Analysis ◽  
Human Decision

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

Once More with Feeling: Augmenting Recognition Primed Decision Making with Affective Factors

2002 ◽  
Vol 46 (3) ◽  
pp. 303-307 ◽  
Author(s):  
Eva Hudlicka ◽  
Jonathan Pfautz
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Personality Traits ◽  
Empirical Evidence ◽  
Computational Models ◽  
Cognitive Architecture ◽  
Human Perception ◽  
Cognitive Engineering ◽  
Affective States ◽  
Affective Factors

Although quintessentially human, emotions have, until recently, been largely ignored in the human factors cognitive engineering / decision-making area. This is surprising, as extensive empirical evidence indicates that emotions, and personality traits, influence human perception and decision-making. This is particularly the case in crisis situations, when extreme affective states may arise (e.g., anxiety). The development of more complete and realistic theories of human perception and decision-making, and associated computational models, will require the inclusion of personality and affective considerations. In this paper, we propose an augmented version of the recognition-primed decision-making theory, which takes into consideration trait and state effects on decision-making. We describe a cognitive architecture that implements this theory, and a generic methodology for modeling trait and state effects within this architecture. Following an initial prototype demonstration, the full architecture is currently being implemented in the context of a military peacekeeping scenario.

scholarly journals Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 727
Author(s):  
Eric J. Ma ◽  
Arkadij Kummer
Keyword(s):  
Decision Making ◽  
High Throughput ◽  
Noisy Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Melting Temperatures ◽  
Hierarchical Bayesian Estimation ◽  
The Face ◽  
Point Data

We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.

scholarly journals The Feeling is Real: Emotion Elicitation in Virtual Reality

2019 ◽  
Vol 63 (1) ◽  
pp. 252-256
Author(s):  
Sahinya Susindar ◽  
Mahnoosh Sadeghi ◽  
Lea Huntington ◽  
Andrew Singer ◽  
Thomas K. Ferris
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Cognitive Processes ◽  
Emotional Responses ◽  
Decision Task ◽  
Affective States ◽  
Desktop Computer ◽  
Emotion Elicitation ◽  
Human Decision ◽  
Display Systems

Classical methods for eliciting emotional responses, including the use of emotionally-charged pictures and films, have been used to study the influence of affective states on human decision-making and other cognitive processes. Advanced multisensory display systems, such as Virtual Reality (VR) headsets, offer a degree of immersion that may support more reliable elicitation of emotional experiences than less-immersive displays, and can provide a powerful yet relatively safe platform for inducing negative emotions such as fear and anger. However, it is not well understood how the presentation medium influences the degree to which emotions are elicited. In this study, emotionally-charged stimuli were introduced via two display configurations – on a desktop computer and on a VR system –and were evaluated based on performance in a decision task. Results show that the use of VR can be a more effective method for emotion elicitation when study decision-making under the influence of emotions.

scholarly journals Approaches to Modeling the Gas-Turbine Maintenance Process

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Tai-Tuck Yu ◽  
James P. Scanlan ◽  
Richard M. Crowder ◽  
Gary B. Wills
Keyword(s):  
Decision Making ◽  
Gas Turbine ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Event Modeling ◽  
Human Decision ◽  
Event Model ◽  
Discrete Event Model

Discrete-event modeling has long been used for logistics and scheduling problems, while multi-agent modeling closely matches human decision-making process. In this paper, a metric-based comparison between the traditional discrete-event and the emerging agent-based modeling approaches is reported. The case study involved the implementation of two functionally identical models based on a realistic, nontrivial, civil aircraft gas turbine global repair operation. The size, structural complexity, and coupling metrics from the two models were used to gauge the benefits and drawbacks of each modeling paradigm. The agent-based model was significantly better than the discrete-event model in terms of execution times, scalability, understandability, modifiability, and structural flexibility. In contrast, and importantly in an engineering context, the discrete-event model guaranteed predictable and repeatable results and was comparatively easy to test because of its single-threaded operation. However, neither modeling approach on its own possesses all these characteristics nor can each handle the wide range of resolutions and scales frequently encountered in problems exemplified by the case study scenario. It is recognized that agent-based modeling can emulate high-level human decision-making and communication closely while discrete-event modeling provides a good fit for low-level sequential processes such as those found in manufacturing and logistics.

scholarly journals Fits-and-Starts: The Changing Nature of the Material City

2022 ◽  
Vol 7 (1) ◽  
pp. 56-71
Author(s):  
Aseem Inam
Keyword(s):  
Decision Making ◽  
21St Century ◽  
Las Vegas ◽  
Human Perception ◽  
Urban Change ◽  
Environmental Costs ◽  
Review Of The Literature ◽  
Early 21St Century ◽  
Space And Time

How and why does the material city in the late 20th and early 21st century change? This article examines one type of prominent urban change, which is “fits-and-starts” and represents change that is concentrated in space and time and that nonetheless has longer term repercussions with high economic and environmental costs. Through a review of the literature and an illuminating case study in Las Vegas, this article reveals how human perception and decision-making via two interrelated phenomena, future speculation and manufactured obsolescence, drive such change. The case study in Las Vegas is particularly fascinating because as a city of apparent extremes, it not only reveals in clear relief phenomena that are present in the capitalist city but it also offers insights into basic patterns of decision-making that actually shape—or design—the contemporary city. The article concludes with more general insights into the nature of this type of urban change and implications for alternative types of urban practices.

Improving Interior-Design Decision-Making in Daylit Spaces

2017 ◽  
pp. 368-388
Author(s):  
Dalia Hafiz
Keyword(s):  
Decision Making ◽  
Interior Design ◽  
Collaborative Design ◽  
Field Measurements ◽  
Decision Support Tool ◽  
Visual Comfort ◽  
Support Tool ◽  
Design Changes ◽  
Traditional Approaches

Case study represents a principle methodology when an in-depth investigation is needed. It can be an alternative to traditional approaches to emphasize the researcher's perspective as central to the process. In an effort to allow for tool application purposefully selected architects and decision-makers were encouraged to apply a new decision-support tool; which that aims at enhancing decision-making though visual comfort evaluation. A selected case study space was used for application: a daylit museum located in Washington DC Metropolitan was examined for visual discomfort problems. Since museums are typically carefully lit because of the sensitivity of exhibits, this case study evaluated the daylighting condition in a museum using a series of illuminance field measurements, simulations and views experienced by occupants along a circulation path through the space. The case study also aimed at understanding how small design changes can affect visual comfort as a tactic for the case studies. A collaborative design effort was used in different stages of the case study.

System Risk Importance Analysis Using Bayesian Networks

2018 ◽  
Vol 25 (01) ◽  
pp. 1850004 ◽  
Author(s):  
Ali Noroozian ◽  
Reza Baradaran Kazemzadeh ◽  
Seyed Taghi Akhavan Niaki ◽  
Enrico Zio
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Structural Characteristics ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Risk Modeling ◽  
Modeling And Analysis ◽  
Importance Analysis ◽  
System Risk

Importance measures (IMs) are used for risk-informed decision making in system operations, safety, and maintenance. Traditionally, they are computed within fault tree (FT) analysis. Although FT analysis is a powerful tool to study the reliability and structural characteristics of systems, Bayesian networks (BNs) have shown explicit advantages in modeling and analytical capabilities. In this paper, the traditional definitions of IMs are extended to BNs in order to have more capability in terms of system risk modeling and analysis. Implementation results on a case study illustrate the capability of finding the most important components in a system.

scholarly journals Principled decision-making workflow with hierarchical Bayesian models of high throughput dose-response measurements

2021 ◽  
Author(s):  
Eric Ma ◽  
Arkadij Kummer
Keyword(s):  
Decision Making ◽  
High Throughput ◽  
Noisy Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Melting Temperatures ◽  
Hierarchical Bayesian Estimation ◽  
The Face ◽  
Point Data

We present a case study applying hierarchical Bayesian estimation on high throughput protein melting point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting temperature posterior distribution estimates to enable principled decision-making in common high throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve fitting. We conclude with a discussion of the relative merits of each workflow.

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